Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations2259077
Missing cells3461559
Missing cells (%)7.7%
Duplicate rows62285
Duplicate rows (%)2.8%
Total size in memory1.8 GiB
Average record size in memory838.7 B

Variable types

Text4
Numeric7
Categorical7
DateTime1
URL1

Alerts

Dataset has 62285 (2.8%) duplicate rowsDuplicates
drive is highly overall correlated with typeHigh correlation
odometer is highly overall correlated with predicted_price and 1 other fieldsHigh correlation
predicted_price is highly overall correlated with odometer and 2 other fieldsHigh correlation
price is highly overall correlated with odometer and 1 other fieldsHigh correlation
type is highly overall correlated with driveHigh correlation
year is highly overall correlated with predicted_priceHigh correlation
title is highly imbalanced (77.3%) Imbalance
fuel is highly imbalanced (75.7%) Imbalance
transmission is highly imbalanced (67.7%) Imbalance
make has 46488 (2.1%) missing values Missing
predicted_price has 568801 (25.2%) missing values Missing
residual has 568801 (25.2%) missing values Missing
condition has 364408 (16.1%) missing values Missing
model has 190148 (8.4%) missing values Missing
paint has 614472 (27.2%) missing values Missing
drive has 663908 (29.4%) missing values Missing
type has 444260 (19.7%) missing values Missing

Reproduction

Analysis started2024-11-26 19:59:48.844520
Analysis finished2024-11-26 20:03:44.974524
Duration3 minutes and 56.13 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

location
Text

Distinct413
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size122.0 MiB
2024-11-26T13:03:45.200940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length7.6488238
Min length2

Characters and Unicode

Total characters17279282
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsfbay
2nd rowphoenix
3rd rowsandiego
4th rowmiami
5th rowjanesville
ValueCountFrequency (%)
miami 201455
 
8.9%
sfbay 140440
 
6.2%
losangeles 103115
 
4.6%
phoenix 91267
 
4.0%
portland 65037
 
2.9%
seattle 64895
 
2.9%
sandiego 60512
 
2.7%
sacramento 56255
 
2.5%
orangecounty 51072
 
2.3%
dallas 48083
 
2.1%
Other values (403) 1376946
61.0%
2024-11-26T13:03:45.534188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1979088
11.5%
e 1643865
 
9.5%
n 1535769
 
8.9%
o 1469402
 
8.5%
s 1337609
 
7.7%
i 1236133
 
7.2%
l 1184796
 
6.9%
t 951524
 
5.5%
r 809476
 
4.7%
m 749089
 
4.3%
Other values (16) 4382531
25.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17279282
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1979088
11.5%
e 1643865
 
9.5%
n 1535769
 
8.9%
o 1469402
 
8.5%
s 1337609
 
7.7%
i 1236133
 
7.2%
l 1184796
 
6.9%
t 951524
 
5.5%
r 809476
 
4.7%
m 749089
 
4.3%
Other values (16) 4382531
25.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17279282
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1979088
11.5%
e 1643865
 
9.5%
n 1535769
 
8.9%
o 1469402
 
8.5%
s 1337609
 
7.7%
i 1236133
 
7.2%
l 1184796
 
6.9%
t 951524
 
5.5%
r 809476
 
4.7%
m 749089
 
4.3%
Other values (16) 4382531
25.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17279282
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1979088
11.5%
e 1643865
 
9.5%
n 1535769
 
8.9%
o 1469402
 
8.5%
s 1337609
 
7.7%
i 1236133
 
7.2%
l 1184796
 
6.9%
t 951524
 
5.5%
r 809476
 
4.7%
m 749089
 
4.3%
Other values (16) 4382531
25.4%

price
Real number (ℝ)

High correlation 

Distinct10776
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10617.864
Minimum255
Maximum74999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2024-11-26T13:03:45.654184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum255
5-th percentile1995
Q14250
median7000
Q313000
95-th percentile31900
Maximum74999
Range74744
Interquartile range (IQR)8750

Descriptive statistics

Standard deviation10264.764
Coefficient of variation (CV)0.96674474
Kurtosis7.6803745
Mean10617.864
Median Absolute Deviation (MAD)3500
Skewness2.4613043
Sum2.3986573 × 1010
Variance1.0536539 × 108
MonotonicityNot monotonic
2024-11-26T13:03:45.775770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4500 52130
 
2.3%
3500 51389
 
2.3%
5500 48868
 
2.2%
6500 48709
 
2.2%
5000 41876
 
1.9%
7500 41849
 
1.9%
2500 40258
 
1.8%
4000 36396
 
1.6%
8500 36350
 
1.6%
3000 35597
 
1.6%
Other values (10766) 1825655
80.8%
ValueCountFrequency (%)
255 1
 
< 0.1%
259 11
 
< 0.1%
260 3
 
< 0.1%
265 2
 
< 0.1%
269 3
 
< 0.1%
270 1
 
< 0.1%
275 28
< 0.1%
277 1
 
< 0.1%
279 1
 
< 0.1%
280 4
 
< 0.1%
ValueCountFrequency (%)
74999 96
< 0.1%
74998 3
 
< 0.1%
74995 31
 
< 0.1%
74990 4
 
< 0.1%
74985 1
 
< 0.1%
74980 1
 
< 0.1%
74950 16
 
< 0.1%
74910 1
 
< 0.1%
74900 109
< 0.1%
74888 1
 
< 0.1%

odometer
Real number (ℝ)

High correlation 

Distinct179952
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133436.09
Minimum1001
Maximum399999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2024-11-26T13:03:45.901460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile30500
Q189594
median131000
Q3174000
95-th percentile242423
Maximum399999
Range398998
Interquartile range (IQR)84406

Descriptive statistics

Standard deviation63804.414
Coefficient of variation (CV)0.4781646
Kurtosis0.27203048
Mean133436.09
Median Absolute Deviation (MAD)42000
Skewness0.3823732
Sum3.014424 × 1011
Variance4.0710032 × 109
MonotonicityNot monotonic
2024-11-26T13:03:46.030852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200000 36354
 
1.6%
150000 29602
 
1.3%
160000 26723
 
1.2%
140000 25054
 
1.1%
180000 24492
 
1.1%
170000 23817
 
1.1%
130000 23431
 
1.0%
120000 21248
 
0.9%
100000 18284
 
0.8%
190000 17885
 
0.8%
Other values (179942) 2012187
89.1%
ValueCountFrequency (%)
1001 7
< 0.1%
1003 2
 
< 0.1%
1004 4
 
< 0.1%
1005 3
 
< 0.1%
1006 5
< 0.1%
1007 3
 
< 0.1%
1008 9
< 0.1%
1009 3
 
< 0.1%
1010 10
< 0.1%
1012 1
 
< 0.1%
ValueCountFrequency (%)
399999 4
< 0.1%
399990 1
 
< 0.1%
399896 1
 
< 0.1%
399842 1
 
< 0.1%
399788 1
 
< 0.1%
399700 1
 
< 0.1%
399699 1
 
< 0.1%
399695 1
 
< 0.1%
399600 1
 
< 0.1%
399559 1
 
< 0.1%

year
Real number (ℝ)

High correlation 

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2008.1785
Minimum1974
Maximum2025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 MiB
2024-11-26T13:03:46.177435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1974
5-th percentile1992
Q12004
median2009
Q32014
95-th percentile2019
Maximum2025
Range51
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.3786356
Coefficient of variation (CV)0.0041722565
Kurtosis1.9659922
Mean2008.1785
Median Absolute Deviation (MAD)5
Skewness-1.0971392
Sum4.5366298 × 109
Variance70.201534
MonotonicityNot monotonic
2024-11-26T13:03:46.295233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2013 128490
 
5.7%
2008 126709
 
5.6%
2007 125668
 
5.6%
2012 124316
 
5.5%
2014 118418
 
5.2%
2006 116073
 
5.1%
2011 113311
 
5.0%
2015 108317
 
4.8%
2010 102656
 
4.5%
2005 100740
 
4.5%
Other values (42) 1094379
48.4%
ValueCountFrequency (%)
1974 4150
0.2%
1975 2907
0.1%
1976 4026
0.2%
1977 4617
0.2%
1978 5163
0.2%
1979 5900
0.3%
1980 3449
0.2%
1981 2926
0.1%
1982 3350
0.1%
1983 3647
0.2%
ValueCountFrequency (%)
2025 210
 
< 0.1%
2024 4681
 
0.2%
2023 15470
 
0.7%
2022 23264
 
1.0%
2021 30892
 
1.4%
2020 36598
 
1.6%
2019 51901
2.3%
2018 63014
2.8%
2017 80185
3.5%
2016 91738
4.1%

long
Real number (ℝ)

Distinct308101
Distinct (%)13.6%
Missing132
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-100.57771
Minimum-177.00763
Maximum142.12326
Zeros2174
Zeros (%)0.1%
Negative2256745
Negative (%)99.9%
Memory size17.2 MiB
2024-11-26T13:03:46.407967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-177.00763
5-th percentile-122.6801
Q1-118.3493
median-97.8575
Q3-80.9167
95-th percentile-73.708
Maximum142.12326
Range319.13089
Interquartile range (IQR)37.4326

Descriptive statistics

Standard deviation19.957097
Coefficient of variation (CV)-0.19842466
Kurtosis-0.10674834
Mean-100.57771
Median Absolute Deviation (MAD)18.4606
Skewness-0.10943245
Sum-2.2719951 × 108
Variance398.28571
MonotonicityNot monotonic
2024-11-26T13:03:46.521184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-80.2926 5279
 
0.2%
-80.2264 4936
 
0.2%
-80.1407 3817
 
0.2%
-80.3165 3573
 
0.2%
-80.1157 3039
 
0.1%
-80.403 2875
 
0.1%
-80.4085 2627
 
0.1%
-80.1891 2573
 
0.1%
-80.2153 2571
 
0.1%
-80.2092 2366
 
0.1%
Other values (308091) 2225289
98.5%
ValueCountFrequency (%)
-177.007631 1
 
< 0.1%
-166.538642 1
 
< 0.1%
-166.1151 1
 
< 0.1%
-165.961268 1
 
< 0.1%
-164.970703 1
 
< 0.1%
-162.7211 1
 
< 0.1%
-162.685547 1
 
< 0.1%
-161.990162 3
< 0.1%
-161.8749 3
< 0.1%
-159.8251 1
 
< 0.1%
ValueCountFrequency (%)
142.12326 1
 
< 0.1%
139.6917 2
< 0.1%
139.3485 1
 
< 0.1%
136.8326 1
 
< 0.1%
128.838981 1
 
< 0.1%
127.7586 2
< 0.1%
127.0706 2
< 0.1%
126.9775 1
 
< 0.1%
126.92395 4
< 0.1%
120.5833 1
 
< 0.1%

lat
Real number (ℝ)

Distinct308041
Distinct (%)13.6%
Missing132
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean36.576177
Minimum-82.921058
Maximum142.58258
Zeros2179
Zeros (%)0.1%
Negative235
Negative (%)< 0.1%
Memory size17.2 MiB
2024-11-26T13:03:46.641897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-82.921058
5-th percentile25.98226
Q133.1098
median36.9695
Q340.9734
95-th percentile46.8829
Maximum142.58258
Range225.50363
Interquartile range (IQR)7.8636

Descriptive statistics

Standard deviation6.5373968
Coefficient of variation (CV)0.17873374
Kurtosis3.1799309
Mean36.576177
Median Absolute Deviation (MAD)3.9391
Skewness-0.31992637
Sum82623572
Variance42.737557
MonotonicityNot monotonic
2024-11-26T13:03:46.756377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.8301 5279
 
0.2%
26.1122 4941
 
0.2%
26.0218 4158
 
0.2%
25.985 3772
 
0.2%
26.2785 3040
 
0.1%
26.152 2840
 
0.1%
25.6694 2627
 
0.1%
25.9894 2559
 
0.1%
26.2674 2362
 
0.1%
27.3215 2225
 
0.1%
Other values (308031) 2225142
98.5%
ValueCountFrequency (%)
-82.921058 1
< 0.1%
-80.172912 1
< 0.1%
-77.911331 1
< 0.1%
-75.747631 1
< 0.1%
-67.980238 1
< 0.1%
-66.787821 1
< 0.1%
-64.633523 1
< 0.1%
-63.477932 1
< 0.1%
-62.712724 1
< 0.1%
-62.428643 1
< 0.1%
ValueCountFrequency (%)
142.582577 1
 
< 0.1%
71.683788 1
 
< 0.1%
71.41821 1
 
< 0.1%
66.742308 1
 
< 0.1%
66.5656 1
 
< 0.1%
65.168 1
 
< 0.1%
65.0907 1
 
< 0.1%
65.07 17
< 0.1%
65.002647 1
 
< 0.1%
64.999013 1
 
< 0.1%

make
Text

Missing 

Distinct65
Distinct (%)< 0.1%
Missing46488
Missing (%)2.1%
Memory size117.2 MiB
2024-11-26T13:03:46.904204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length5.869565
Min length3

Characters and Unicode

Total characters12986935
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowbmw
2nd rowford
3rd rowbuick
4th rowford
5th rowmini
ValueCountFrequency (%)
ford 338920
15.2%
toyota 257162
 
11.6%
chevrolet 253404
 
11.4%
honda 159476
 
7.2%
nissan 127411
 
5.7%
jeep 91056
 
4.1%
bmw 83121
 
3.7%
mercedes-benz 68322
 
3.1%
ram 67864
 
3.0%
gmc 67864
 
3.0%
Other values (58) 710478
31.9%
2024-11-26T13:03:47.149207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 1488077
 
11.5%
e 1226277
 
9.4%
a 1110565
 
8.6%
r 967700
 
7.5%
t 850280
 
6.5%
d 847006
 
6.5%
n 726607
 
5.6%
s 611655
 
4.7%
c 586474
 
4.5%
h 532245
 
4.1%
Other values (17) 4040049
31.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12986935
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1488077
 
11.5%
e 1226277
 
9.4%
a 1110565
 
8.6%
r 967700
 
7.5%
t 850280
 
6.5%
d 847006
 
6.5%
n 726607
 
5.6%
s 611655
 
4.7%
c 586474
 
4.5%
h 532245
 
4.1%
Other values (17) 4040049
31.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12986935
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1488077
 
11.5%
e 1226277
 
9.4%
a 1110565
 
8.6%
r 967700
 
7.5%
t 850280
 
6.5%
d 847006
 
6.5%
n 726607
 
5.6%
s 611655
 
4.7%
c 586474
 
4.5%
h 532245
 
4.1%
Other values (17) 4040049
31.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12986935
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1488077
 
11.5%
e 1226277
 
9.4%
a 1110565
 
8.6%
r 967700
 
7.5%
t 850280
 
6.5%
d 847006
 
6.5%
n 726607
 
5.6%
s 611655
 
4.7%
c 586474
 
4.5%
h 532245
 
4.1%
Other values (17) 4040049
31.1%

title
Categorical

Imbalance 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.7 MiB
clean
2052321 
rebuilt
 
101686
salvage
 
76605
lien
 
19924
missing
 
6626

Length

Max length10
Median length5
Mean length5.1591291
Min length4

Characters and Unicode

Total characters11654870
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowclean
2nd rowclean
3rd rowclean
4th rowclean
5th rowlien

Common Values

ValueCountFrequency (%)
clean 2052321
90.8%
rebuilt 101686
 
4.5%
salvage 76605
 
3.4%
lien 19924
 
0.9%
missing 6626
 
0.3%
parts only 1915
 
0.1%

Length

2024-11-26T13:03:47.261875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-26T13:03:47.366538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
clean 2052321
90.8%
rebuilt 101686
 
4.5%
salvage 76605
 
3.4%
lien 19924
 
0.9%
missing 6626
 
0.3%
parts 1915
 
0.1%
only 1915
 
0.1%

Most occurring characters

ValueCountFrequency (%)
l 2252451
19.3%
e 2250536
19.3%
a 2207446
18.9%
n 2080786
17.9%
c 2052321
17.6%
i 134862
 
1.2%
t 103601
 
0.9%
r 103601
 
0.9%
u 101686
 
0.9%
b 101686
 
0.9%
Other values (8) 265894
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11654870
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 2252451
19.3%
e 2250536
19.3%
a 2207446
18.9%
n 2080786
17.9%
c 2052321
17.6%
i 134862
 
1.2%
t 103601
 
0.9%
r 103601
 
0.9%
u 101686
 
0.9%
b 101686
 
0.9%
Other values (8) 265894
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11654870
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 2252451
19.3%
e 2250536
19.3%
a 2207446
18.9%
n 2080786
17.9%
c 2052321
17.6%
i 134862
 
1.2%
t 103601
 
0.9%
r 103601
 
0.9%
u 101686
 
0.9%
b 101686
 
0.9%
Other values (8) 265894
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11654870
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 2252451
19.3%
e 2250536
19.3%
a 2207446
18.9%
n 2080786
17.9%
c 2052321
17.6%
i 134862
 
1.2%
t 103601
 
0.9%
r 103601
 
0.9%
u 101686
 
0.9%
b 101686
 
0.9%
Other values (8) 265894
 
2.3%

predicted_price
Real number (ℝ)

High correlation  Missing 

Distinct880257
Distinct (%)52.1%
Missing568801
Missing (%)25.2%
Infinite0
Infinite (%)0.0%
Mean9654.2327
Minimum-130010.67
Maximum199744.8
Zeros0
Zeros (%)0.0%
Negative32469
Negative (%)1.4%
Memory size17.2 MiB
2024-11-26T13:03:47.478266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-130010.67
5-th percentile1323.6216
Q13984.4621
median6970.3885
Q312147.898
95-th percentile27928.542
Maximum199744.8
Range329755.47
Interquartile range (IQR)8163.4361

Descriptive statistics

Standard deviation9229.3266
Coefficient of variation (CV)0.95598759
Kurtosis10.463864
Mean9654.2327
Median Absolute Deviation (MAD)3592.1458
Skewness2.5012176
Sum1.6318318 × 1010
Variance85180470
MonotonicityNot monotonic
2024-11-26T13:03:47.594952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5902.958331 360
 
< 0.1%
13114.58296 310
 
< 0.1%
5252.8994 310
 
< 0.1%
5639.426842 210
 
< 0.1%
4997.341533 185
 
< 0.1%
7498.932142 169
 
< 0.1%
28448.35148 160
 
< 0.1%
16001.02651 153
 
< 0.1%
6506.118276 145
 
< 0.1%
33370.6295 141
 
< 0.1%
Other values (880247) 1688133
74.7%
(Missing) 568801
 
25.2%
ValueCountFrequency (%)
-130010.6665 1
 
< 0.1%
-126408.7028 1
 
< 0.1%
-112889.5666 2
 
< 0.1%
-84341.24129 1
 
< 0.1%
-82038.97127 1
 
< 0.1%
-81208.26793 1
 
< 0.1%
-76723.25057 5
< 0.1%
-65224.41556 1
 
< 0.1%
-64493.15741 1
 
< 0.1%
-63327.52873 1
 
< 0.1%
ValueCountFrequency (%)
199744.8004 1
< 0.1%
185005.8282 1
< 0.1%
162719.2224 1
< 0.1%
154389.4149 1
< 0.1%
149271.8248 1
< 0.1%
143567.4825 1
< 0.1%
140640.0716 1
< 0.1%
138479.5499 1
< 0.1%
132867.4969 1
< 0.1%
131402.7434 1
< 0.1%

residual
Real number (ℝ)

Missing 

Distinct1241173
Distinct (%)73.4%
Missing568801
Missing (%)25.2%
Infinite0
Infinite (%)0.0%
Mean921.42951
Minimum-198054.8
Maximum147708.27
Zeros0
Zeros (%)0.0%
Negative693904
Negative (%)30.7%
Memory size17.2 MiB
2024-11-26T13:03:47.714642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-198054.8
5-th percentile-5661.2037
Q1-1129.7161
median536.63518
Q32468.2644
95-th percentile9074.0227
Maximum147708.27
Range345763.07
Interquartile range (IQR)3597.9805

Descriptive statistics

Standard deviation5462.0434
Coefficient of variation (CV)5.927793
Kurtosis24.403018
Mean921.42951
Median Absolute Deviation (MAD)1783.7305
Skewness0.62787115
Sum1.5574702 × 109
Variance29833918
MonotonicityNot monotonic
2024-11-26T13:03:47.836285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15385.41704 306
 
< 0.1%
587.0416687 170
 
< 0.1%
47.10060034 165
 
< 0.1%
1401.067858 165
 
< 0.1%
898.9734885 150
 
< 0.1%
-13483.06932 140
 
< 0.1%
-476.7844254 135
 
< 0.1%
1939.803574 132
 
< 0.1%
-7814.969437 125
 
< 0.1%
346.788378 125
 
< 0.1%
Other values (1241163) 1688663
74.8%
(Missing) 568801
 
25.2%
ValueCountFrequency (%)
-198054.8004 1
< 0.1%
-183505.8282 1
< 0.1%
-161029.2224 1
< 0.1%
-129867.4969 1
< 0.1%
-116675.5318 1
< 0.1%
-115695.425 1
< 0.1%
-113567.4825 1
< 0.1%
-99209.1648 1
< 0.1%
-97829.47848 1
< 0.1%
-97167.04486 1
< 0.1%
ValueCountFrequency (%)
147708.2679 1
 
< 0.1%
133308.7028 1
 
< 0.1%
132010.6665 1
 
< 0.1%
127889.5666 2
 
< 0.1%
113223.2506 5
< 0.1%
99538.97127 1
 
< 0.1%
96841.24129 1
 
< 0.1%
93293.15741 1
 
< 0.1%
93001.32433 1
 
< 0.1%
89346.94801 1
 
< 0.1%

time_posted
Date

Distinct1831229
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Memory size17.2 MiB
Minimum2023-11-18 08:00:36+00:00
Maximum2024-11-16 17:07:29+00:00
2024-11-26T13:03:47.960190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:48.163226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

condition
Categorical

Missing 

Distinct6
Distinct (%)< 0.1%
Missing364408
Missing (%)16.1%
Memory size119.9 MiB
excellent
793640 
good
705423 
like new
228300 
fair
142714 
salvage
 
13168

Length

Max length9
Median length8
Mean length6.5912072
Min length3

Characters and Unicode

Total characters12488156
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowexcellent
2nd rowgood
3rd rowgood
4th rowgood
5th rowgood

Common Values

ValueCountFrequency (%)
excellent 793640
35.1%
good 705423
31.2%
like new 228300
 
10.1%
fair 142714
 
6.3%
salvage 13168
 
0.6%
new 11424
 
0.5%
(Missing) 364408
16.1%

Length

2024-11-26T13:03:48.282904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-26T13:03:48.383635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
excellent 793640
37.4%
good 705423
33.2%
new 239724
 
11.3%
like 228300
 
10.8%
fair 142714
 
6.7%
salvage 13168
 
0.6%

Most occurring characters

ValueCountFrequency (%)
e 2862112
22.9%
l 1828748
14.6%
o 1410846
11.3%
n 1033364
 
8.3%
x 793640
 
6.4%
c 793640
 
6.4%
t 793640
 
6.4%
g 718591
 
5.8%
d 705423
 
5.6%
i 371014
 
3.0%
Other values (8) 1177138
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12488156
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2862112
22.9%
l 1828748
14.6%
o 1410846
11.3%
n 1033364
 
8.3%
x 793640
 
6.4%
c 793640
 
6.4%
t 793640
 
6.4%
g 718591
 
5.8%
d 705423
 
5.6%
i 371014
 
3.0%
Other values (8) 1177138
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12488156
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2862112
22.9%
l 1828748
14.6%
o 1410846
11.3%
n 1033364
 
8.3%
x 793640
 
6.4%
c 793640
 
6.4%
t 793640
 
6.4%
g 718591
 
5.8%
d 705423
 
5.6%
i 371014
 
3.0%
Other values (8) 1177138
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12488156
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2862112
22.9%
l 1828748
14.6%
o 1410846
11.3%
n 1033364
 
8.3%
x 793640
 
6.4%
c 793640
 
6.4%
t 793640
 
6.4%
g 718591
 
5.8%
d 705423
 
5.6%
i 371014
 
3.0%
Other values (8) 1177138
9.4%

url
URL

Distinct1969478
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Memory size293.3 MiB
https://miami.craigslist.org/mdc/cto/d/miami-2018-ford-mustang-convertible/7762499156.html
 
110
https://miami.craigslist.org/pbc/cto/d/boca-raton-2012-kia-rio/7762474333.html
 
95
https://miami.craigslist.org/pbc/cto/d/boca-raton-2006-gmc-sierra-1500-sle/7759974947.html
 
95
https://miami.craigslist.org/pbc/cto/d/deerfield-beach-2005-scion-xb-in-fair/7758116281.html
 
95
https://miami.craigslist.org/mdc/cto/d/miami-2005-lexus-ls430/7761198368.html
 
90
Other values (1969473)
2258592 
ValueCountFrequency (%)
https://miami.craigslist.org/mdc/cto/d/miami-2018-ford-mustang-convertible/7762499156.html 110
 
< 0.1%
https://miami.craigslist.org/pbc/cto/d/boca-raton-2012-kia-rio/7762474333.html 95
 
< 0.1%
https://miami.craigslist.org/pbc/cto/d/boca-raton-2006-gmc-sierra-1500-sle/7759974947.html 95
 
< 0.1%
https://miami.craigslist.org/pbc/cto/d/deerfield-beach-2005-scion-xb-in-fair/7758116281.html 95
 
< 0.1%
https://miami.craigslist.org/mdc/cto/d/miami-2005-lexus-ls430/7761198368.html 90
 
< 0.1%
https://miami.craigslist.org/mdc/cto/d/miami-2011-ford-mustang/7760134705.html 90
 
< 0.1%
https://miami.craigslist.org/pbc/cto/d/lake-worth-2004-bmw-325i-ice-cold-ac/7754977134.html 85
 
< 0.1%
https://miami.craigslist.org/brw/cto/d/fort-lauderdale-lexus-ct200h-2015/7759810242.html 85
 
< 0.1%
https://miami.craigslist.org/mdc/cto/d/miami-2019-gmc-acadia/7760605245.html 85
 
< 0.1%
https://miami.craigslist.org/mdc/cto/d/hallandale-hyundai-sonata-2017/7755236994.html 80
 
< 0.1%
Other values (1969468) 2258167
> 99.9%
ValueCountFrequency (%)
https 2259077
100.0%
ValueCountFrequency (%)
miami.craigslist.org 201461
 
8.9%
sfbay.craigslist.org 140662
 
6.2%
losangeles.craigslist.org 103127
 
4.6%
phoenix.craigslist.org 91298
 
4.0%
portland.craigslist.org 65051
 
2.9%
seattle.craigslist.org 64930
 
2.9%
sandiego.craigslist.org 60529
 
2.7%
sacramento.craigslist.org 56347
 
2.5%
orangecounty.craigslist.org 51072
 
2.3%
dallas.craigslist.org 48093
 
2.1%
Other values (405) 1376507
60.9%
ValueCountFrequency (%)
/mdc/cto/d/miami-2018-ford-mustang-convertible/7762499156.html 110
 
< 0.1%
/pbc/cto/d/boca-raton-2012-kia-rio/7762474333.html 95
 
< 0.1%
/pbc/cto/d/boca-raton-2006-gmc-sierra-1500-sle/7759974947.html 95
 
< 0.1%
/pbc/cto/d/deerfield-beach-2005-scion-xb-in-fair/7758116281.html 95
 
< 0.1%
/mdc/cto/d/miami-2005-lexus-ls430/7761198368.html 90
 
< 0.1%
/mdc/cto/d/miami-2011-ford-mustang/7760134705.html 90
 
< 0.1%
/pbc/cto/d/lake-worth-2004-bmw-325i-ice-cold-ac/7754977134.html 85
 
< 0.1%
/brw/cto/d/fort-lauderdale-lexus-ct200h-2015/7759810242.html 85
 
< 0.1%
/mdc/cto/d/miami-2019-gmc-acadia/7760605245.html 85
 
< 0.1%
/mdc/cto/d/hallandale-hyundai-sonata-2017/7755236994.html 80
 
< 0.1%
Other values (1969468) 2258167
> 99.9%
ValueCountFrequency (%)
2259077
100.0%
ValueCountFrequency (%)
2259077
100.0%

state
Text

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size123.8 MiB
2024-11-26T13:03:48.549983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length14
Mean length8.4474168
Min length4

Characters and Unicode

Total characters19083365
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCalifornia
2nd rowArizona
3rd rowCalifornia
4th rowFlorida
5th rowWisconsin
ValueCountFrequency (%)
california 534433
20.9%
florida 291973
 
11.4%
new 174426
 
6.8%
texas 150205
 
5.9%
arizona 126315
 
4.9%
washington 103465
 
4.1%
oregon 101656
 
4.0%
york 93579
 
3.7%
colorado 75793
 
3.0%
carolina 58960
 
2.3%
Other values (45) 842407
33.0%
2024-11-26T13:03:48.826516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2646665
13.9%
i 2379989
12.5%
o 1982513
10.4%
n 1561889
 
8.2%
r 1531112
 
8.0%
l 1156653
 
6.1%
e 889622
 
4.7%
s 792355
 
4.2%
C 708142
 
3.7%
f 557168
 
2.9%
Other values (36) 4877257
25.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19083365
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2646665
13.9%
i 2379989
12.5%
o 1982513
10.4%
n 1561889
 
8.2%
r 1531112
 
8.0%
l 1156653
 
6.1%
e 889622
 
4.7%
s 792355
 
4.2%
C 708142
 
3.7%
f 557168
 
2.9%
Other values (36) 4877257
25.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19083365
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2646665
13.9%
i 2379989
12.5%
o 1982513
10.4%
n 1561889
 
8.2%
r 1531112
 
8.0%
l 1156653
 
6.1%
e 889622
 
4.7%
s 792355
 
4.2%
C 708142
 
3.7%
f 557168
 
2.9%
Other values (36) 4877257
25.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19083365
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2646665
13.9%
i 2379989
12.5%
o 1982513
10.4%
n 1561889
 
8.2%
r 1531112
 
8.0%
l 1156653
 
6.1%
e 889622
 
4.7%
s 792355
 
4.2%
C 708142
 
3.7%
f 557168
 
2.9%
Other values (36) 4877257
25.6%

model
Text

Missing 

Distinct1283
Distinct (%)0.1%
Missing190148
Missing (%)8.4%
Memory size113.6 MiB
2024-11-26T13:03:49.045361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length31
Median length26
Mean length5.6330942
Min length1

Characters and Unicode

Total characters11654472
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique69 ?
Unique (%)< 0.1%

Sample

1st rowx1
2nd rowexcursion
3rd rowverano
4th rowe350
5th rowclubman
ValueCountFrequency (%)
1500 71162
 
3.2%
f150 67969
 
3.0%
silverado 52885
 
2.4%
camry 44876
 
2.0%
accord 44177
 
2.0%
r 41027
 
1.8%
2500 40682
 
1.8%
civic 39144
 
1.8%
wrangler 37629
 
1.7%
f250 31463
 
1.4%
Other values (940) 1764330
78.9%
2024-11-26T13:03:49.387961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1181512
 
10.1%
r 1147741
 
9.8%
e 1011281
 
8.7%
o 730789
 
6.3%
c 674533
 
5.8%
t 601475
 
5.2%
s 592841
 
5.1%
i 563196
 
4.8%
n 553343
 
4.7%
0 505374
 
4.3%
Other values (31) 4092387
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11654472
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1181512
 
10.1%
r 1147741
 
9.8%
e 1011281
 
8.7%
o 730789
 
6.3%
c 674533
 
5.8%
t 601475
 
5.2%
s 592841
 
5.1%
i 563196
 
4.8%
n 553343
 
4.7%
0 505374
 
4.3%
Other values (31) 4092387
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11654472
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1181512
 
10.1%
r 1147741
 
9.8%
e 1011281
 
8.7%
o 730789
 
6.3%
c 674533
 
5.8%
t 601475
 
5.2%
s 592841
 
5.1%
i 563196
 
4.8%
n 553343
 
4.7%
0 505374
 
4.3%
Other values (31) 4092387
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11654472
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1181512
 
10.1%
r 1147741
 
9.8%
e 1011281
 
8.7%
o 730789
 
6.3%
c 674533
 
5.8%
t 601475
 
5.2%
s 592841
 
5.1%
i 563196
 
4.8%
n 553343
 
4.7%
0 505374
 
4.3%
Other values (31) 4092387
35.1%

paint
Categorical

Missing 

Distinct12
Distinct (%)< 0.1%
Missing614472
Missing (%)27.2%
Memory size117.1 MiB
white
403912 
black
286518 
silver
235085 
grey
213031 
blue
177369 
Other values (7)
328690 

Length

Max length6
Median length5
Mean length4.7564023
Min length3

Characters and Unicode

Total characters7822403
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwhite
2nd rowblack
3rd rowwhite
4th rowwhite
5th rowgreen

Common Values

ValueCountFrequency (%)
white 403912
17.9%
black 286518
12.7%
silver 235085
 
10.4%
grey 213031
 
9.4%
blue 177369
 
7.9%
red 155680
 
6.9%
green 59294
 
2.6%
brown 47663
 
2.1%
custom 35842
 
1.6%
yellow 14956
 
0.7%
Other values (2) 15255
 
0.7%
(Missing) 614472
27.2%

Length

2024-11-26T13:03:49.502075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
white 403912
24.6%
black 286518
17.4%
silver 235085
14.3%
grey 213031
13.0%
blue 177369
10.8%
red 155680
 
9.5%
green 59294
 
3.6%
brown 47663
 
2.9%
custom 35842
 
2.2%
yellow 14956
 
0.9%
Other values (2) 15255
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e 1333876
17.1%
l 734675
 
9.4%
r 726008
 
9.3%
i 638997
 
8.2%
b 511550
 
6.5%
w 466531
 
6.0%
t 439754
 
5.6%
h 403912
 
5.2%
c 322360
 
4.1%
a 295982
 
3.8%
Other values (11) 1948758
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7822403
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1333876
17.1%
l 734675
 
9.4%
r 726008
 
9.3%
i 638997
 
8.2%
b 511550
 
6.5%
w 466531
 
6.0%
t 439754
 
5.6%
h 403912
 
5.2%
c 322360
 
4.1%
a 295982
 
3.8%
Other values (11) 1948758
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7822403
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1333876
17.1%
l 734675
 
9.4%
r 726008
 
9.3%
i 638997
 
8.2%
b 511550
 
6.5%
w 466531
 
6.0%
t 439754
 
5.6%
h 403912
 
5.2%
c 322360
 
4.1%
a 295982
 
3.8%
Other values (11) 1948758
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7822403
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1333876
17.1%
l 734675
 
9.4%
r 726008
 
9.3%
i 638997
 
8.2%
b 511550
 
6.5%
w 466531
 
6.0%
t 439754
 
5.6%
h 403912
 
5.2%
c 322360
 
4.1%
a 295982
 
3.8%
Other values (11) 1948758
24.9%

fuel
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing9
Missing (%)< 0.1%
Memory size112.6 MiB
gas
2054961 
diesel
 
128318
hybrid
 
49622
electric
 
20677
other
 
5490

Length

Max length8
Median length3
Mean length3.2869258
Min length3

Characters and Unicode

Total characters7425389
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgas
2nd rowgas
3rd rowgas
4th rowdiesel
5th rowgas

Common Values

ValueCountFrequency (%)
gas 2054961
91.0%
diesel 128318
 
5.7%
hybrid 49622
 
2.2%
electric 20677
 
0.9%
other 5490
 
0.2%
(Missing) 9
 
< 0.1%

Length

2024-11-26T13:03:49.601790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-26T13:03:49.686970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
gas 2054961
91.0%
diesel 128318
 
5.7%
hybrid 49622
 
2.2%
electric 20677
 
0.9%
other 5490
 
0.2%

Most occurring characters

ValueCountFrequency (%)
s 2183279
29.4%
g 2054961
27.7%
a 2054961
27.7%
e 303480
 
4.1%
i 198617
 
2.7%
d 177940
 
2.4%
l 148995
 
2.0%
r 75789
 
1.0%
h 55112
 
0.7%
y 49622
 
0.7%
Other values (4) 122633
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7425389
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 2183279
29.4%
g 2054961
27.7%
a 2054961
27.7%
e 303480
 
4.1%
i 198617
 
2.7%
d 177940
 
2.4%
l 148995
 
2.0%
r 75789
 
1.0%
h 55112
 
0.7%
y 49622
 
0.7%
Other values (4) 122633
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7425389
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 2183279
29.4%
g 2054961
27.7%
a 2054961
27.7%
e 303480
 
4.1%
i 198617
 
2.7%
d 177940
 
2.4%
l 148995
 
2.0%
r 75789
 
1.0%
h 55112
 
0.7%
y 49622
 
0.7%
Other values (4) 122633
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7425389
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 2183279
29.4%
g 2054961
27.7%
a 2054961
27.7%
e 303480
 
4.1%
i 198617
 
2.7%
d 177940
 
2.4%
l 148995
 
2.0%
r 75789
 
1.0%
h 55112
 
0.7%
y 49622
 
0.7%
Other values (4) 122633
 
1.7%

drive
Categorical

High correlation  Missing 

Distinct3
Distinct (%)< 0.1%
Missing663908
Missing (%)29.4%
Memory size114.6 MiB
4wd
634934 
fwd
564101 
rwd
396134 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4785507
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfwd
2nd row4wd
3rd rowfwd
4th rowrwd
5th rowfwd

Common Values

ValueCountFrequency (%)
4wd 634934
28.1%
fwd 564101
25.0%
rwd 396134
17.5%
(Missing) 663908
29.4%

Length

2024-11-26T13:03:49.782462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-26T13:03:49.871227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
4wd 634934
39.8%
fwd 564101
35.4%
rwd 396134
24.8%

Most occurring characters

ValueCountFrequency (%)
w 1595169
33.3%
d 1595169
33.3%
4 634934
 
13.3%
f 564101
 
11.8%
r 396134
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4785507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
w 1595169
33.3%
d 1595169
33.3%
4 634934
 
13.3%
f 564101
 
11.8%
r 396134
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4785507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
w 1595169
33.3%
d 1595169
33.3%
4 634934
 
13.3%
f 564101
 
11.8%
r 396134
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4785507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
w 1595169
33.3%
d 1595169
33.3%
4 634934
 
13.3%
f 564101
 
11.8%
r 396134
 
8.3%

type
Categorical

High correlation  Missing 

Distinct14
Distinct (%)< 0.1%
Missing444260
Missing (%)19.7%
Memory size117.2 MiB
sedan
490546 
SUV
489445 
pickup
227602 
truck
151324 
coupe
100198 
Other values (9)
355702 

Length

Max length11
Median length9
Mean length5.0283345
Min length3

Characters and Unicode

Total characters9125507
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSUV
2nd rowsedan
3rd rowvan
4th rowwagon
5th rowhatchback

Common Values

ValueCountFrequency (%)
sedan 490546
21.7%
SUV 489445
21.7%
pickup 227602
10.1%
truck 151324
 
6.7%
coupe 100198
 
4.4%
hatchback 98399
 
4.4%
convertible 71424
 
3.2%
van 69050
 
3.1%
minivan 46538
 
2.1%
wagon 33142
 
1.5%
Other values (4) 37149
 
1.6%
(Missing) 444260
19.7%

Length

2024-11-26T13:03:49.976378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sedan 490546
27.0%
suv 489445
27.0%
pickup 227602
12.5%
truck 151324
 
8.3%
coupe 100198
 
5.5%
hatchback 98399
 
5.4%
convertible 71424
 
3.9%
van 69050
 
3.8%
minivan 46538
 
2.6%
wagon 33142
 
1.8%
Other values (4) 37149
 
2.0%

Most occurring characters

ValueCountFrequency (%)
a 850346
 
9.3%
n 767742
 
8.4%
e 752368
 
8.2%
c 747346
 
8.2%
p 555402
 
6.1%
d 499566
 
5.5%
s 494647
 
5.4%
S 489445
 
5.4%
U 489445
 
5.4%
V 489445
 
5.4%
Other values (15) 2989755
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9125507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 850346
 
9.3%
n 767742
 
8.4%
e 752368
 
8.2%
c 747346
 
8.2%
p 555402
 
6.1%
d 499566
 
5.5%
s 494647
 
5.4%
S 489445
 
5.4%
U 489445
 
5.4%
V 489445
 
5.4%
Other values (15) 2989755
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9125507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 850346
 
9.3%
n 767742
 
8.4%
e 752368
 
8.2%
c 747346
 
8.2%
p 555402
 
6.1%
d 499566
 
5.5%
s 494647
 
5.4%
S 489445
 
5.4%
U 489445
 
5.4%
V 489445
 
5.4%
Other values (15) 2989755
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9125507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 850346
 
9.3%
n 767742
 
8.4%
e 752368
 
8.2%
c 747346
 
8.2%
p 555402
 
6.1%
d 499566
 
5.5%
s 494647
 
5.4%
S 489445
 
5.4%
U 489445
 
5.4%
V 489445
 
5.4%
Other values (15) 2989755
32.8%

transmission
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size124.3 MiB
automatic
2027207 
manual
217483 
other
 
14387

Length

Max length9
Median length9
Mean length8.6857137
Min length5

Characters and Unicode

Total characters19621696
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowautomatic
2nd rowautomatic
3rd rowautomatic
4th rowautomatic
5th rowmanual

Common Values

ValueCountFrequency (%)
automatic 2027207
89.7%
manual 217483
 
9.6%
other 14387
 
0.6%

Length

2024-11-26T13:03:50.088844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-26T13:03:50.177863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
automatic 2027207
89.7%
manual 217483
 
9.6%
other 14387
 
0.6%

Most occurring characters

ValueCountFrequency (%)
a 4489380
22.9%
t 4068801
20.7%
u 2244690
11.4%
m 2244690
11.4%
o 2041594
10.4%
i 2027207
10.3%
c 2027207
10.3%
n 217483
 
1.1%
l 217483
 
1.1%
h 14387
 
0.1%
Other values (2) 28774
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19621696
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4489380
22.9%
t 4068801
20.7%
u 2244690
11.4%
m 2244690
11.4%
o 2041594
10.4%
i 2027207
10.3%
c 2027207
10.3%
n 217483
 
1.1%
l 217483
 
1.1%
h 14387
 
0.1%
Other values (2) 28774
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19621696
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4489380
22.9%
t 4068801
20.7%
u 2244690
11.4%
m 2244690
11.4%
o 2041594
10.4%
i 2027207
10.3%
c 2027207
10.3%
n 217483
 
1.1%
l 217483
 
1.1%
h 14387
 
0.1%
Other values (2) 28774
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19621696
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4489380
22.9%
t 4068801
20.7%
u 2244690
11.4%
m 2244690
11.4%
o 2041594
10.4%
i 2027207
10.3%
c 2027207
10.3%
n 217483
 
1.1%
l 217483
 
1.1%
h 14387
 
0.1%
Other values (2) 28774
 
0.1%

Interactions

2024-11-26T13:03:23.706667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:10.099169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:12.454617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:14.815621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:17.142821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:19.445553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:21.709215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:23.981956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:10.450181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:12.827646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:15.133796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:17.477822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:19.819549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:21.985474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:24.258929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:10.833188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:13.153777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:15.449647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:17.855811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:20.165646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:22.266723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:24.528978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:11.168288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:13.492872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:15.827148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:18.174987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:20.494793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:22.545722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:24.842143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:11.519324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:13.875872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:16.234747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:18.504069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:20.860815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:22.847914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:25.106983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:11.835480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:14.149287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:16.502534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:18.808259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:21.124098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:23.124150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:25.371253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:12.112909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:14.438653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:16.811680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:19.101448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:21.401359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T13:03:23.396467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-26T13:03:50.245682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
conditiondrivefuellatlongodometerpaintpredicted_pricepriceresidualtitletransmissiontypeyear
condition1.0000.0150.0460.0420.0290.2070.0370.1680.2000.0500.1070.0480.0540.197
drive0.0151.0000.1390.0970.0380.0890.1040.0870.1950.1010.0570.0730.5150.207
fuel0.0460.1391.0000.0190.0460.1210.0690.0710.1490.1200.0260.1760.2420.109
lat0.0420.0970.0191.000-0.1240.0640.012-0.068-0.0320.0890.0520.0280.025-0.102
long0.0290.0380.046-0.1241.000-0.0080.028-0.004-0.070-0.1340.0630.0280.0450.073
odometer0.2070.0890.1210.064-0.0081.0000.036-0.615-0.5050.0770.0790.0540.088-0.376
paint0.0370.1040.0690.0120.0280.0361.0000.0350.0490.0200.0230.0810.0980.105
predicted_price0.1680.0870.071-0.068-0.004-0.6150.0351.0000.820-0.1600.0440.0440.0500.577
price0.2000.1950.149-0.032-0.070-0.5050.0490.8201.0000.3230.0720.0290.0970.477
residual0.0500.1010.1200.089-0.1340.0770.020-0.1600.3231.0000.0350.0400.060-0.033
title0.1070.0570.0260.0520.0630.0790.0230.0440.0720.0351.0000.0340.0400.103
transmission0.0480.0730.1760.0280.0280.0540.0810.0440.0290.0400.0341.0000.1930.197
type0.0540.5150.2420.0250.0450.0880.0980.0500.0970.0600.0400.1931.0000.113
year0.1970.2070.109-0.1020.073-0.3760.1050.5770.477-0.0330.1030.1970.1131.000

Missing values

2024-11-26T13:03:27.060792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-26T13:03:30.024779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-26T13:03:37.125009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

locationpriceodometeryearlonglatmaketitlepredicted_priceresidualtime_postedconditionurlstatemodelpaintfueldrivetypetransmission
0sfbay8000.0107000.02014.0-122.263537.5364bmwclean9149.465612-1149.4656122024-11-14 08:40:06+00:00excellenthttps://sfbay.craigslist.org/pen/cto/d/redwood-city-bmw-x1-2014/7802085178.htmlCaliforniax1whitegasfwdSUVautomatic
1phoenix11900.0212000.02000.0-111.919833.3092fordclean5604.2721496295.7278512024-11-13 06:31:08+00:00goodhttps://phoenix.craigslist.org/wvl/cto/d/tempe-ford-excursion/7801735227.htmlArizonaexcursionblackgas4wdNoneautomatic
2sandiego9800.058000.02014.0-117.097833.1605buickclean5590.2108834209.7891172024-11-10 19:25:57+00:00goodhttps://sandiego.craigslist.org/nsd/cto/d/escondido-2014-buick-verano-turbo/7801080470.htmlCaliforniaveranowhitegasfwdsedanautomatic
3miami18500.049500.02010.0-80.092826.4116fordclean13353.8566255146.1433752024-10-28 11:01:13+00:00goodhttps://miami.craigslist.org/pbc/cto/d/boca-raton-ford-diesel-heavy-duty-only/7797221853.htmlFloridae350whitedieselrwdvanautomatic
4janesville6500.097000.02016.0-88.998042.6969minilien7159.862329-659.8623292024-10-26 22:00:00+00:00goodhttps://janesville.craigslist.org/cto/d/janesville-2016-mini-cooper-clubman/7796901812.htmlWisconsinclubmangreengasfwdwagonmanual
5palmsprings4250.0153800.02009.0-116.534733.8414scionclean4544.302114-294.3021142024-10-25 08:01:50+00:00excellenthttps://palmsprings.craigslist.org/cto/d/palm-springs-2009-scion-xd-silver/7796418026.htmlCaliforniaxdsilvergasfwdhatchbackautomatic
6dallas4800.0150000.02015.0-96.795532.7086jeepclean5630.785633-830.7856332024-10-23 16:45:23+00:00like newhttps://dallas.craigslist.org/dal/cto/d/dallas-2015-jeep-patriot-runs-great/7795969760.htmlTexaspatriotNonegasNoneSUVautomatic
7lubbock5700.0100000.02013.0-101.833333.5260nissanclean8659.426468-2959.4264682024-10-23 16:07:58+00:00excellenthttps://lubbock.craigslist.org/cto/d/lubbock-2013-nissan-murano-low-100k/7795958166.htmlTexasmuranowhitegasNoneSUVautomatic
8orangecounty5999.0147000.02017.0-117.829833.6829dodgeclean6941.637505-942.6375052024-10-12 08:02:38+00:00excellenthttps://orangecounty.craigslist.org/cto/d/irvine-2017-dodge-journey-se/7792718372.htmlCaliforniajourneywhitegasfwdSUVautomatic
9pittsburgh2300.085009.02005.0-80.087640.4717chryslerclean3588.751782-1288.7517822024-10-04 12:48:11+00:00excellenthttps://pittsburgh.craigslist.org/cto/d/mc-kees-rocks-2006-chrysler-pt-cruiser/7790500366.htmlPennsylvaniapt cruisersilvergasfwdconvertiblemanual
locationpriceodometeryearlonglatmaketitlepredicted_priceresidualtime_postedconditionurlstatemodelpaintfueldrivetypetransmission
2259067orangecounty21500.045500.02013.0-117.78650033.905800chevroletclean24249.860163-2749.8601632023-12-12 10:55:34+00:00like newhttps://orangecounty.craigslist.org/cto/d/yorba-linda-2013-chevrolet-silverado/7697086462.htmlCaliforniasilverado 1500silvergasrwdtruckautomatic
2259068chicago3500.098452.02002.0-87.61425041.672789chevroletclean7545.087024-4045.0870242024-09-12 10:51:11+00:00goodhttps://chicago.craigslist.org/chc/cto/d/chicago-2002-chevrolet-1500/7784113491.htmlIllinoissilverado 1500NonegasrwdNoneautomatic
2259069visalia18500.070000.02009.0-119.03150036.068600chevroletclean17445.4919591054.5080412024-03-24 13:51:00+00:00excellenthttps://visalia.craigslist.org/cto/d/porterville-2009-silverado-1500-lt/7730470178.htmlCaliforniasilverado 1500whitegasrwdtruckautomatic
2259070bellingham2800.0196000.01993.0-122.92780048.611100chevroletclean271.6660452528.3339552023-12-24 14:49:12+00:00goodhttps://bellingham.craigslist.org/cto/d/orcas-1993-chevy-x4/7700964734.htmlWashingtonsilverado 1500whitegas4wdtruckautomatic
2259071ithaca5900.0153000.02008.0-76.41154242.467108chevroletclean8164.384625-2264.3846252024-05-04 21:03:10+00:00goodhttps://ithaca.craigslist.org/cto/d/etna-2008-chevrolet-silverado-1500-with/7743805658.htmlNew Yorksilverado 1500bluegas4wdpickupautomatic
2259072miami3500.0205342.02012.0-80.41300025.700000chevroletcleanNaNNaN2024-11-13 17:43:11+00:00Nonehttps://miami.craigslist.org/mdc/cto/d/miami-chevrolet-silverado/7801918708.htmlFloridasilverado 1500NonegasNonepickupautomatic
2259073dallas15000.073000.02013.0-96.78280032.769100chevroletclean20057.705778-5057.7057782024-01-20 16:38:43+00:00like newhttps://dallas.craigslist.org/dal/cto/d/dallas-2013-chevrolet-silverado-1500/7709578896.htmlTexassilverado 1500whitegasrwdpickupautomatic
2259074sacramento26900.064235.02007.0-122.34000040.547000chevroletclean16545.61534210354.3846582024-05-04 08:05:56+00:00like newhttps://sacramento.craigslist.org/cto/d/redding-2007-chevy-silverado-wheel/7743609333.htmlCaliforniasilverado 1500blackgasrwdtruckautomatic
2259075inlandempire11000.0230000.02003.0-117.13871634.588333chevroletclean3449.8016727550.1983282024-04-28 14:15:16+00:00goodhttps://inlandempire.craigslist.org/cto/d/apple-valley-chevy-silverado/7741764548.htmlCaliforniasilverado 1500greygasfwdtruckautomatic
2259076reno8950.0160000.01998.0-119.77640039.497200chevroletclean5992.4596042957.5403962024-07-28 09:24:02+00:00excellenthttps://reno.craigslist.org/cto/d/reno-1998-chevy-step-side-short-bed-4x4/7770228753.htmlNevadasilverado 1500Nonegas4wdpickupautomatic

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11925miami1500.0180000.02005.0-80.14235626.295185scionclean2606.942247-1106.9422472024-06-18 12:29:42+00:00fairhttps://miami.craigslist.org/pbc/cto/d/deerfield-beach-2005-scion-xb-in-fair/7758116281.htmlFloridaxbgreygasfwdsedanautomatic95
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33414miami8500.0154000.02006.0-80.08440026.346200gmcclean7957.950019542.0499812024-06-24 13:37:00+00:00goodhttps://miami.craigslist.org/pbc/cto/d/boca-raton-2006-gmc-sierra-1500-sle/7759974947.htmlFlorida1500greygasrwdpickupautomatic95
17297miami3500.0228000.02005.0-80.35880025.734300lexusclean3605.466910-105.4669102024-06-28 11:07:17+00:00goodhttps://miami.craigslist.org/mdc/cto/d/miami-2005-lexus-ls430/7761198368.htmlFloridalssilvergasfwdsedanautomatic90
25753miami5900.0101000.02011.0-80.40460025.596800fordrebuilt10683.655656-4783.6556562024-06-24 21:56:57+00:00like newhttps://miami.craigslist.org/mdc/cto/d/miami-2011-ford-mustang/7760134705.htmlFloridamustangblackgasrwdconvertiblemanual90
13916miami2500.0220000.02004.0-80.19500026.585000bmwcleanNaNNaN2024-06-08 08:51:23+00:00NaNhttps://miami.craigslist.org/pbc/cto/d/lake-worth-2004-bmw-325i-ice-cold-ac/7754977134.htmlFlorida2blackgasNaNsedanautomatic85
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42255miami14900.044000.02019.0-80.41862925.598488gmcrebuilt20861.233815-5961.2338152024-06-26 12:51:10+00:00like newhttps://miami.craigslist.org/mdc/cto/d/miami-2019-gmc-acadia/7760605245.htmlFloridaacadiasilvergasfwdSUVautomatic85
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